发表机构
National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文评论马蒂亚斯·马斯的《全球人工智能治理架构》，指出全球AI治理的核心问题不是缺乏架构，而是由权力分散、激励各异的多方行为者塑造的新兴架构。
AI 中文摘要
人工智能给全球治理带来了一个异常困难的问题。该技术发展迅速，易于跨境传播，并受到其资源和能力可能与国家相媲美的行为者的影响。然而，国际回应仍然支离破碎、代表性不均，且绝大多数不具有约束力。因此，挑战不仅在于确定适当的规则或制度，还在于理解谁有能力并有动力去创建、执行和调整它们。这篇评论文章通过马蒂亚斯·马斯（Matthijs Maas）的《全球人工智能治理架构》（Architectures of Global AI Governance）来审视这些问题。马斯提出了一个雄心勃勃的框架，通过社会技术变革、治理中断和制度复杂性等视角来思考人工智能治理。他的论述有效地抵制了技术决定论和对单一制度蓝图的追求，转而强调一个碎片化且不断演变的治理架构的可能性。然而，该文章认为，制度设计不能与权力分配相分离。马斯经常援引“我们”应该对人工智能做什么，但这一集体主体掩盖了国家、国际机构和科技公司之间的重要差异。国家在市场、基础设施、战略投入以及企业本身方面保留着强大的权力。与此同时，许多关于前沿人工智能的具有深远影响的决策——构建什么、构建速度多快、采取何种保障措施以及何时发布——都集中在少数私营公司手中。因此，全球人工智能治理的核心问题可能与其说是没有建筑师的架构，不如说是一个由拥有不同形式权力、不同激励且没有共同计划的多方行为者塑造的新兴架构。
英文摘要
Artificial intelligence presents an unusually difficult problem for global governance. The technology develops rapidly, crosses borders easily, and is shaped by actors whose resources and capabilities may rival those of states. Yet international responses remain fragmented, unevenly representative, and overwhelmingly non-binding. The challenge is therefore not simply to identify appropriate rules or institutions, but to understand who has the capacity and incentive to create, enforce, and adapt them. This review essay examines these questions through Matthijs Maas's Architectures of Global AI Governance. Maas offers an ambitious framework for thinking about AI governance through the lenses of sociotechnical change, governance disruption, and regime complexity. His account usefully resists both technological determinism and the search for a single institutional blueprint, emphasizing instead the possibilities of a fragmented and evolving governance architecture. The essay argues, however, that institutional design cannot be separated from the distribution of power. Maas frequently invokes what "we" should do about AI, but that collective subject obscures important differences among states, international institutions, and technology companies. States retain formidable powers over markets, infrastructure, strategic inputs, and firms themselves. At the same time, many consequential decisions about frontier AI - what is built, how quickly, with what safeguards, and when it is released - are concentrated within a small number of private companies. The central problem of global AI governance may therefore be less architecture without an architect than an emerging architecture shaped by multiple actors possessing different forms of power, divergent incentives, and no common set of plans.
